This paper introduces a quadruped robot, Tachyon, which aims to achieve high payload, robust, and dynamic locomotion on the various terrain with high energy efficiency. Thanks to a novel compact series-parallel elastic actuator (SPEA) on the upper link and a four-bar linkage design in the knee joint for constant vertical foot force, the 41-kg robot can carry more than 20 kg of payloads with dynamic walking. The combination of a robust horizontal CoM stabilizer and SPEA joint torque controller provides low impedance force controllability of the whole-body even when only the knee joint can accurately detect its joint torque. The major performance of Tachyon is demonstrated by carrying a 20-kg rice bag—half of its body weight—and climbing stairs with dynamic locomotion. When the robot climbs the stairs, the SPEA parallel spring improves energy efficiency by 16% and is also effective for various other gaits. The robustness of the robot is also shown by its high flexibility and fall avoidance capability when an unknown disturbance of 400 N or more is applied.

Manipulating objects whose physical properties are unknown remains one of the greatest challenges in robotics. Controlling grasp force is an essential aspect of handling unknown objects without slipping or crushing them. Although extensive research has been carried out on grasp force control, unknown object manipulation is still difficult because conventional approaches assume that object properties (mass, center of gravity, friction coefficient, etc.) are known for grasp force control. One of the approaches to address this issue is incipient slip detection. However, there has been few detailed investigations of robust detection and control of incipient slip on rotational case. This study makes contributions on deriving the theoretical model of incipient slip and proposes a new algorithm to detect incipient slip. Additionally, a novel sensor configuration and a grasp force control algorithm based on the derived theoretical model are proposed. Finally, the proposed algorithm is evaluated by grasping objects with different weights and moments including a fragile pastry (éclair).

Motion planning is important in a wide variety of applications such as robotic manipulation. However, it is still challenging to reliably find a collision-free path within a reasonable time. To address the issue, this paper proposes a novel framework which combines a sampling-based planner and deep learning for faster motion planning, focusing on heuristics. The proposed method extends Task-Space Rapidly-exploring Random Trees (TS-RRT) to guide the trees with a "heuristic map" where every voxel has a cost-to-go value toward the goal. It also utilizes fully convolutional neural networks (CNNs) for producing more appropriate heuristic maps, rather than manually-designed heuristics. To verify the effectiveness of the proposed method, experiments for motion planning using a real environment and mobile manipulator are carried out. The results indicate that it outperforms the existing planners, especially in terms of the average planning time with smaller variance.

This paper proposes a control framework for a two-armed and two-wheeled mobile robot that can coordinate all the joint forces to achieve diverse motion objectives such as position, velocity, acceleration, force and impedance at any part of the body. The framework comprises two components: 1) Generalized Inverse Dynamics (GID) that determines joint forces satisfying multiple objectives considering task priorities and various constraints and 2) the Idealized Joint Unit (IJU) that generates accurate torque with assigned apparent inertia and viscosity. GID treats the robot as a single manipulator with multiple branches using a joint model with 2-DOF motion subspace equivalent to two opposing wheels. A 21-DOF mobile robot equipped with IJUs is set up and GID is applied to it. The result shows that GID works well in the examples of physical human-robot interaction and object manipulation where motion objectives are attained coordinating the whole body while keeping passivity due to the redundancy and the assigned impedance property.

This paper proposes a multi-objective, highly generalized and efficient control framework for a virtual character performing various physical interactions in a dynamically simulated world. The framework comprises 1) a generalized inverse dynamics that determines joint forces satisfying multiple objectives considering priorities, unactuated joints and inequality constraints about contacts, 2) a generalized stabilizer available in various contact situations based on the long-term momentum stabilization and 3) a motion primitive network for realizing composite motions by modularizing and interconnecting motion functions. By applying the proposed framework, various interactions with a virtual humanoid, such as basic reflections and carrying objects, are realized in real time with a tactile sensation through the two-armed multi-fingered haptic device we developed.

We have been developing a small biped entertainment robot and expanding its capabilities for adaptability and mobility in the home environment with enhanced core technologies. Conspicuous enhancements are a new robot actuator, ISA and Real-time Integrated Adaptive Motion Control System as a comprehensive motion control for SDR to realize the dynamic and smooth/elegant motion performances. In order to evaluate the basic motion control capabilities we propose a new motion evaluating system for a small biped entertainment robot. The system is composed of the SDR Motion Base and the control system. We have developed a serial-parallel hybrid type mechanism to apply for the basic experiment of SDR's motion control capabilities. We discuss the design concept of the motion evaluating system and the experimental method using the proposed Motion Base.

This paper proposes the integrated motion control method to make a bipedal humanoid walk, jump and run. This method generates dynamically consistent motion patterns in real-time based on the concept of the dynamics filter, which assures that the force and the moment generated by the robot can equilibrate with that caused by the environment. The validity of the algorithm is verified by the dynamic simulation. The proposed method is applied to the real humanoid "QRIO" under the adaptive controls, and stable walking, jumping and running including the transitions between them are realized.

SDR-4X is the latest prototype model, which remains a small humanoid type robot and is expanding its capabilities for the adaptability in home environment. Some of the new key technologies have been developed for SDR-4X. The first is newly developed small robot actuators named ISA-4, the second is real-time integrated adaptive motion control. The third is a motion creating software system, which allows us to create SDR's attractive motion performances. The fourth technology is real-time real-world space perception, and the fifth is multi-modal human robot interaction. These five technologies enable to realize a capable robot that can be adopted with human in home environment. In addition, we have explored and developed entertainment applications using these technologies. A cappella chorus performance and a high-tempo dance performance are introduced as the attractive applications.

SDR-4X is a small biped entertainment robot with expanded capabilities for a variety of motion performances. We developed the SDR motion creating system and the whole body adaptive motion control for SDR. This software system makes it easy to produce creative motion performances that include stable biped walking and dance steps synchronized to music. The motion creating system is composed of two parts: the motion editor used to edit upper body motion and whole body motion, and the foot trajectory editor used to create stable lower body motion. Once the upper and lower body motions are created, the system allows the motion designer to inspection the whole body motion before applying it to SDR-4X. The process of creating a fast-paced dance performance is introduced as an attractive application of this software system.

A robot body is not only an active unit but also a sensing unit. This paper describes a humanoid that integrates stereo vision, full-body tactile sensor, sound sensors and wrist force sensors into a sensor image. The sensor image reflects the current state of the environment and is regarded as all the input to the robot brain in this system. The brain program processes a series of sensor images and controls its body using them. The robot is remote-brained and designed to be a testbed to do research on sensor based behaviors of a full-body humanoid. This paper describes the design concept and the details of the system.

We describe the method in which a visually guided swing motion for a 16 DOF two-armed bipedal robot is acquired by applying a GA (genetic algorithm) to a NN (neural network) controller. The evolutionary approach to the acquisition of various motions for robots has been successfully used by many researchers, but most studies have been carried out only through computer simulations. In this research, we adopt a real robot with a complicated body used in a noisy environment. The evolutionary processes are examined in. A virtual world constructed on a CRS-CS6400 parallel computer which simulates such factors as swing dynamics, visual processes noise reduction processes, and time lags in a control system. It took about and hours for an artificial evolution to create a successfully individual after 50 generations from an initial population of 200 unsuccessful genes. Using the NN decoded from the most successful individual of the last generation, a real two-armed bipedal robot that could swing successfully was obtained.

In this paper, we describe the design implementation of this real-time layer for remote-brained robot. "Remote-brained approach" is a paradigm for software research by separating the robot brain from the robot body. The interface from the body to the brain top-level software is also denoted. Furthermore, an application of the reactive motion of humanoid-type robot is described. The real-time facility is fundamentally important for a robot that behaves in the read-world. To accomplish both large scale intelligent software and real-time functionality, we design the system by dividing the brain into a low-level real-time layer and a high-level action decision layer. The action decision layer is put on a WS, and the real-time layer is put on a transputer network.

We present design and implementation of a tactile sensor system, sensor suit, that covers the entire body of a robot. The sensor suit is designed to be soft and flexible and to have a large number of sensing regions. We have built the sensor suit using electrically conductive fabric and string. The current version of the sensor suit has 192 sensing regions. Each sensing region works as a binary switch in the current version. All of the signals from the sensor suit are gathered and superimposed on a visual image of the robot. The video multiplexer for the sensor signals is built on a field programmable gate array set. The construction of sensor suit system and its application with a full-body humanoid are presented.

We present a vision-based real-time control system of a human-form robot in swing motion. The robot has 16 DOF joints and a camera which is designed to do research on human-form robot behaviors. The key idea to develop the vision based system for the robots are the remote-brained approach and real-time visual tracking. In this approach, the robot system is designed to have the brain and the body separate and to connect them by a radio link. The body sends the visual image to the brain and the body receives the motion, references. The basic algorithm to observe the swing motion by vision uses the optical flow generation. The control algorithm for flow-based visual feedback is described.